I. Executive Summary
In recognizing the impacts of climate change and monitoring the environment to achieve Sustainable Development Goals collaborative solutions are required. This calls for standardized data and tools. But how can we ensure an effective exchange of reliable information across disciplines without sacrificing individual users’ needs? Climate services require vast volumes of data from different providers to be processed by various scientific ecosystems: Raw data needs to be transformed into analysis ready data and from there into indicators to become more useful for supporting decisions. To provide a processing infrastructure that supports collaboration, we need standards based on the principles of being findable, accessible, interoperable, and reusable. OGC standards are aligned with these principles allowing for the reuse of data refinement features across political boundaries, organizations, and administrative levels.
Policy instruments should include technological guidelines, for example mandate the use of international standards for data formats, metadata and machine to machine communication protocols, in order to foster interoperability and software reuse. This would strengthen international collaboration on software development, and in turn contribute to the deployment of effective, robust and scientifically credible climate resilience information systems. With increased data access and interoperability of data, processing tools and data infrastructure, it can reduce human and economic costs and ensure appropriate policies are implemented to benefit all.
The OGC Climate Resilience Pilot, as the initial phase of a series of long-term climate initiatives, aimed to transform geospatial data, technologies, and other capabilities into meaningful information for various stakeholders, including decision makers, scientists, policy makers, data providers, software developers, and service providers. The pilot demonstrated the establishment of data pipelines to convert vast amounts of raw data through various steps into decision-ready information. To get to decision ready information the data first needs to be organised from multiple sources into processing pipelines, towards analysis-ready formats. The importance of analysis-ready data and decision-ready indicators was emphasized through discussions on various aspects of GEODataCubes. The pilot explored scientific aspects of climate impact by examining case studies related to droughts, floods, and wildfires, highlighting assessment tools and the complexities of climate indices. Ultimately, this Climate Resilience Pilot serves as a valuable resource for making informed decisions to support and enhance climate action. It specifically assists the location community in developing powerful visualization and communication tools to effectively address ongoing climate threats such as heat, drought, floods, and wildfires.
One of the biggest gaps to date has been the challenge of translating the outputs of global climate models into specific impacts and risks at the local level. The climate modeling community has embraced standards and there is a wide array of data for modelers to exchange and compare, with numerous climate data services now available online. However, outside the weather and climate domain, planners and GIS analysts working for agencies responsible for climate change impacts have limited familiarity with and capacity to consume climate model results. Because of this, a key focus of this pilot was exploring methods for extracting essential climate variables (ECVs) from climate model output scenarios and transforming them into a form more readily consumable via GIS platforms and applied to the local level. Climate variables relevant to use case impacts were selected. Climate variable data cubes were extracted into temporal and spatial ranges specific to the use cases. Finally the data structure was transformed from multidimensional grided cubes into data forms more readily consumable by geospatial applications. For example open standards were employed such as 2D OGC geopackage and geojson point data and published to OGC API services — making them readily available and explorable by a much wider user community. These pilot data flows serve as useful examples of how climate model results can be translated into impacts and risks at the local level in a way that is easy to integrate into existing planning workflows.
With the target user group of non-technical decision makers, the workflow from data to visualisation is being shown at several chapters of this report. A dedicated chapter is pointing out the options and challenges of usage on artificial intelligence to establish a 5D meta world where the efficiency of climate action can be simulated. Reduction of disaster risks du to technical engineering constructions like dams are able to be simulated. Climate resilience is not only an aspect of shift of meteorological phenomena but also related to land degradation and loss of biodiversity. Therefor the vegetation is been pointed out and options of 3D vegetation simulation is shown and how different species are surviving under changing climate conditions. It could be shown that small scale urban planning is being supported by the data to visualisation application where single tree species are representing the real or simulated situation of a small scale area. The pilot is showing studies of Los Angeles.
The pilot recognizes the significant challenges associated with effectively conveying information to decision-makers, which necessitates a thorough examination of communication methods. As a result, a dedicated chapter has been incorporated into the pilot’s work to address this issue. This chapter emphasizes unique approaches that facilitate effective communication with non-technical individuals who frequently hold responsibility for local climate resilience action strategies. By focusing on communication, the pilot aims to bridge the gap between technical and non-technical stakeholders, ensuring that vital information is conveyed accurately and comprehensively. The inclusion of this chapter serves as a testament to the pilot’s aim to enhancing communication strategies for improved decision-making in the realm of climate resilience.
Based on the generated data information, there are several areas of focus and further exploration for the scenario tests and analysis in the context of climate data processing. Here is a breakdown of the key points that should be addressed in follow-on activities:
II. Keywords
The following are keywords to be used by search engines and document catalogues.
Climate Resilience, data, ARD, component, use case, FAIR, Drought, Heat, Fire, Floods
III. Security considerations
No security considerations have been made for this document.
Engineering report for OGC Climate Resilience Pilot
1. Introduction
1.1. Enhancing Interoperability for Climate Resilience Information Systems
The OGC Climate Resilience Pilot will be the first phase of multiple long term climate activities aiming to evolve geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers so we can make valuable, informed decisions to improve climate action. The goal is to help the location community develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, fires as well as supporting the national determined contributions for greenhouse gas emission reduction. Climate resilience is often considered the use case of our lifetime, and the OGC community is uniquely positioned to accelerate solutions through collective problem solving with this initiative.
Figure 1
As illustrated, big, raw data from multiple sources requires further processing in order to be ready for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables, leads to “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable building blocks following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate Change Services Pilots will also illustrate the graphical exploration of the Decision Ready Climate Data. It will demonstrate how to design FAIR climate services information systems. The OGC Pilot demonstrators will illustrate the necessary tools and the visualizations to address climate actions moving towards climate resilience.
1.2. The Role of the Pilot
The OGC Climate Resilience Community brings decision makers, scientists, policy makers, data providers, software developers, and service providers together. The goal is to enable everyone to take the relevant actions to address climate change and make well informed decisions for climate change adaptation. This includes scientists, decision makers, city managers, politicians, and last but not least, it includes everyone of us. So what do we need? We need data from lots of organizations, available at different scales for large and small areas to be integrated with scientific processes, analytical models, and simulation environments. We need data visualization and communication tools to shape the message in the right way for any client. Many challenges can be met through resources that adhere to FAIR principles. FAIR as in: Findable, Accessible, Interoperable, and Reusable. No single organization has all the data we need to understand the consequences of climate change. The OGC Climate Resilience Community identifies, discusses, and develops these resources. The OGC community builds the guidebooks and Best Practices, it experiments with new technologies to share data and information, and collaboratively addresses shared challenges.
The OGC Climate Resilience Community has a vision to support efforts on climate actions and enable international partnerships (SDG 17), and move towards global interoperable open digital infrastructures providing climate resilience information on users demand. This pilot will contribute to establishing an OGC climate resilience concept store for the community where all appropriate climate information to build climate resilience information systems as open infrastructures can be found in one place, be it Information about data services, tools, software, handbooks, or a place to discuss experiences and needs. The concept store covers all phases of Climate Resilience, from initial hazards identification and mapping to vulnerability and risk analysis to options assessments, prioritization, and planning, and ends with implementation planning and monitoring capabilities. These major challenges can only be met through the combined efforts of many OGC members across government, industry, and academia.
This Call for Participation solicits interests from organizations to join the upcoming Climate Resilience Pilot, an OGC Collaborative Solution and Innovation Program activity. This six-months Pilot is setting the stage for a series of follow up activities. It therefore focuses on use-case development, implementation, and exploration. It answers questions such as:
What use-cases can be realized with the current data, services, analytical functions, and visualization capabilities that we have?
How much effort is it to realize these use-cases?
What is missing, or needs to be improved, in order to transfer the use-cases developed in the pilot to other areas?
1.3. Objectives
The pilot has three objectives. First, to better understand what is currently possible with the available data and technology. Second, what additional data and technology needs to be developed in future to better meet the needs of the Climate Resilience Community; and third, to capture Best Practices, and to allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions.
1.4. Background
With growing local communities, an increase in climate-driven disasters, and an increasing risk of future natural hazards, the demand for National Resilience Frameworks and Climate Resilience Information Systems (CRIS) cannot be overstated. Climate Resilience Information Systems (CRIS) are enabling data-search, -fetch, -fusion, -processing and -visualization. They enable access, understanding, and use of federal data, facilitate integration of federal and state data with local data, and serve as local information hubs for climate resilience knowledge sharing.
CRIS are already existing and operational, like the Copernicus Climate Change Service with the Climate Data Store. CRIS architectures can be further enhanced by providing climate scientific methods and visualization capabilities as climate building blocks. Based on FAIR principles, these building blocks enable in particular the reusability of Climate Resilience Information Systems features and capabilities. Reusability is an essential component when goals, expertises, and resources are aligned from the national to the local level. Framework conditions differ across the country, but building blocks enable as much reuse of existing Best Practices, tools, data, and services as possible.
Goals and objectives of decision makers vary at different scales. At the municipal level, municipal leaders and citizens directly face climate-related hazards. Aspects thus come into focus such as reducing vulnerability and risk, building resilience through local measures, or enhancing emergency response. At the state level, the municipal efforts can be coordinated and supported by providing funding and enacting relevant policies. The national, federal, or international level provides funding, science data, and international coordination to enable the best analysis and decisions at the lower scales.
Figure 2
Productivity and decision making are enhanced when climate building blocks are exchangeable across countries, organizations, or administrative levels (see Figure below). This OGC Climate Resilience Pilot is a contribution towards an open, multi-level infrastructure that integrates data spaces, open science, and local-to-international requirements and objectives. It contributes to the technology and governance stack that enables the integration of data including historical observations, real time sensing data, reanalyses, forecasts or future projections. It addresses data-to-decision pipelines, data analysis and representation, and bundles everything in climate resilience building blocks. These building blocks are complemented by Best Practices, guidelines, and cook-books that enable multi–stakeholder decision making for the good of society in a changing natural environment.
The OGC Innovation Program brings all groups together: The various members of the stakeholder group define use cases and requirements, the technologists and data providers experiment with new tools and data products in an agile development process. The scientific community provides results in appropriate formats and enables open science by providing applications that can be parameterized and executed on demand.
Figure 3
This OGC Climate Resilience Pilot is part of the OGC Climate Community Collaborative Solution and Innovation process, an open community process that uses the OGC as the governing body for collaborative activities among all members. A spiral approach is applied to connect technology enhancements, new data products, and scientific research with community needs and framework conditions at different scales. The spiral approach defines real world use cases, identifies gaps, produces new technology and data, and tests these against the real world use cases before entering the next iteration. Evaluation and validation cycles alternate and continuously define new work tasks. These tasks include documentation and toolbox descriptions on the consumer side, and data and service offerings, interoperability, and system architecture developments on the producer side. It is emphasized that research and development is not constrained to the data provider or infrastructure side. Many tasks need to be executed on the data consumer side in parallel and then merged with advancements on the provider side in regular intervals.
Good experiences have been made using OGC API standards in the past. For example, the remote operations on climate simulations (roocs) use OGC API Processes for subsetting data sets to reduce the data volume being transported. Other systems use OGC STAC for metadata and data handling or OGC Earth Observation Exploitation Platform Best Practices for the deployment of climate building blocks or applications into CRIS architectures. Still data handling regarding higher complex climate impact assessments within FAIR and open infrastructures needs to be enhanced. There is no international recommendation or best practice on usage of existing API standards within individual CRIS. It is the goal of this pilot to contribute to the development of such a recommendation, respecting existing operational CRIS that are serving heterogen user groups
Figure 4
1.5. Climate Indices
To make planning decisions to build resilience and adapt to future climate, government officials from local to national, as well as corporate leaders and citizens need an approachable yet scientifically rigorous view of their local climate. We propose a dynamic web mapping interface and report generation tool backed by a suite of web services and downloadable data. All data and web service deliverables will be provided following FAIR principles at no cost, using the appropriate OGC standards.
The climate indices describe 47 measures of future temperature and precipitation in 3 future time periods (early, mid, late century) under 2 emission scenarios RCP 4.5 and 8.5. These indices were created to inform understanding of 5 climate hazards (Wildfire, Heat, Drought, Inland Flooding, Coastal Inundation). Wildfire and drought are the current focus of the disaster pilot and these climate indices will provide useful in those projects when considering future climate.
The project will present a pattern with reproducible workflows in an open Github repo showing the full process of transforming climate science data (CMIP model outputs) into a collection of analysis ready data layers (47 temperature and precipitation indices) and transforming those into decision ready information as climate indices summarized to local geographies such as counties and other subnational boundaries.
1.6. Technical Challenges
Realizing the delivery of Decision Ready Data on demand to achieve Climate Resilience involves a number of technical challenges that have already been identified by the community. A subset will be selected and embedded in use-cases that will be defined jointly by Pilot Sponsors and the OGC team. The goal is to ensure a clear value-enhancement pipeline as illustrated in Figure 1, above. This includes, among other elements, a baseline of standardised operators for data reduction and analytics. These need to fit into an overall workflow that provides translation services between upstream model data and downstream output — basically from raw data, to analysis-ready data, to decision-ready data. The following technical challenges have been identified and will be treated in the focus areas cycles of the Pilot accordingly:
Big Data Challenge: Multiple obstacles still exist, creating big barriers for seamless information delivery starting from Data Discovery. Here the emergence of new data platforms, new processing functionalities, and thus new products, data discovery remains a challenge. In addition to existing solutions based on established metadata profiles and catalog services, new technologies such as OGC’s Spatio-Temporal Asset Catalog (STAC) and open Web APIs such as OGC API Records will be explored. Furthermore, aspects of Data Access need to be solved where the new OGC API suite of Web APIs for data access, subsetting, and processing are currently utilized very successfully in several domains. Several code sprints have shown that server-side solutions can be realized within days and clients can interact very quickly with these server endpoints, thus development time is radically reduced. A promising specialized candidate for climate data and non-climate data integration has been recently published in the form of the OGC API — Environmental Data Retrieval (EDR). But which additional APIs are needed for climate data? Is the current set of OGC APIs sufficiently qualified to support the data enhancement pipeline illustrated in Figure 1? If not, what modifications and extensions need to be made available? How do OGC APIs cooperate with existing technologies such as THREDDS and OPEnDAP? For challenges of data spaces, Data Cubes have recently been explored in the OGC data cube workshop. Ad hoc creation and embedded processing functions have been identified as essential ingredients for efficient data exploration and exchange. Is it possible to transfer these concepts to all stages of the processing pipeline? How to scale both ways from local, ad hoc cubes to pan-continental cubes and vice versa. How to extend cubes as part of data fusion and data integration processes?
Cross-Discipline Data Integration: Different disciplines such as Earth Observation, various social science, or climate modeling use different conceptual models in their data collection, production, and analytical processes. How can we map between these different models? What patterns have been used to transform conceptual models to logical models, and eventually physical models? The production of modern Decision-ready information needs the integration of several data sets, including census and demographics, further social science data, transportation infrastructure, hydrography, land use, topography and other data sets. This pilot cycle uses ‘location’ as the common denominator between these diverse data sets and works with several data providers and scientific disciplines. In terms of Data Exchange Formats the challenge is to know what data formats need to be supported at the various interfaces of the processing pipeline? What is the minimum constellation of required formats to cover the majority of use cases? What role do container formats play? Challenging on technical level is also the Data Provenance. Many archives include data from several production cycles, such as IPCC AR 5 and AR 6 models. In this context, long term support needs to be realized and full traceability from high level data products back to the original raw data. Especially in context of reliable data based policy, clear audit trails and accountability for the data to information evolution needs to be ensured.
Building Blocks for processing pipelines: With a focus on Machine Learning and Artificial Intelligence which plays an increasing role in the context of data science and data integration. This focus area needs to evaluate the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges e.g. on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling & data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place?
1.7. How is this Pilot Relevant to the Climate Resilience Domain Working Group?
The Climate Resilience DWG will concern itself with technology and technology policy issues, focusing on geospatial information and technology interests as related to climate mitigation and adaptation as well as the means by which those issues can be appropriately factored into the OGC standards development process.
The mission of the Climate Resilience DWG is to identify geospatial interoperability issues and challenges that impede climate action, then examine ways in which those challenges can be met through application of existing OGC Standards, or through development of new geospatial interoperability standards under the auspices of OGC.
Activities to be undertaken by the Climate Resilience DWG include but are not limited to:
Identify the OGC interface standards and encodings useful to apply FAIR concepts to climate change services platforms;
Liaise with other OGC Working Groups (WGs) to drive standards evolution;
Promote the usage of the aforementioned standards with climate change service providers and policy makers addressing international regional and local needs;
Liaise with external groups working on technologies relevant to establishing ecosystems of EO Exploitation Platforms;
Liaise with external groups working on relevant technologies;
Publish OGC Technical Papers, Discussion Papers or Best Practices on interoperable interfaces for climate change services;
Provide software toolkits to facilitate the deployment of climate change services platforms.
2. Contributors
| Name | Organization | Role or Summary of contribution |
|---|---|---|
| Guy Schumann | RSS-Hydro | Lead ER Editor |
| Albert Kettner | RSS-Hydro/DFO | Lead ER Editor |
| Timm Dapper | Laubwerk GmbH | |
| Zhe Fang | Wuhan University | |
| Hanwen Xu | Wuhan University | |
| Peng Yue | Wuhan University | |
| Dean Hintz | Safe Software, Inc. | |
| Kailin Opaleychuk | Safe Software, Inc. | |
| Jérôme Jacovella-St-Louis | Ecere Corporation | |
| Hanna Krimm | alpS GmbH | |
| Andrew Lavender | Pixalytics Ltd | Development of drought indicator |
| Samantha Lavender | Pixalytics Ltd | Development of drought indicator |
| Jenny Cocks | Pixalytics Ltd | Development of drought indicator |
| Jakub Walawender | Walawender, Jakub P. | |
| Eugene Yu | GMU | |
| Gil Heo | GMU | |
| Glenn Laughlin | Pelagis Data Solutions | Coastal Resilience & Climate Adaptation |
| Patrick Dion | Ecere | |
| Tom Landry | Intact Financial Corporation | |
| Nils Hempelmann | OGC | Climate resilience Pilot Coordinator |
2.1. About Laubwerk
Laubwerk is a software development company whose mission is to combine accurate, broadly applicable visualizations of vegetation with deeper information and utility that goes far beyond their visual appearance. We achieve this through building a database that combines ultra-realisting 3D representation of plants with extensive metadata that represents plant properties. This unique combination makes Laubwerk a prime partner to bridge the gap from data-driven simulation to eye-catching visualizations.
2.2. About Pixalytics Ltd
Pixalytics Ltd is an independent consultancy company specializing in Earth Observation (EO). We combine cutting-edge scientific knowledge with satellite and airborne data to provide answers to questions about our planet’s resources and behavior. The underlying work includes developing algorithms and software, with activities including a focus on EO quality control and end-user focused applications.
2.3. About Safe Software
Safe Software is a leader in supporting geospatial interoperability and automation for more than 25 years as creators of the FME platform. FME was created to promote FAIR principles, including data sharing across barriers and silos, with unparalleled support for a wide array of both vendor specific formats and open standards. Within this platform, Safe Software provides a range of tools to support interoperability workflows. FME Form is a graphical authoring environment that allows users to rapidly prototype transformation workflows in a no-code environment. FME Flow then allows users to publish data transforms to enterprise oriented service architectures. FME Hosted offers a low cost, easy to deploy and scalable environment for deploying transformation and integration services to the cloud.
Open standards have always been a core strategy for Safe to better support data sharing. The FME platform can be seen as a bridge between the many supported vendor protocols and open standards such as XML, JSON and OGC standards such as GML, KML, WMS, WFS and OGC APIs. Safe has collaborated extensively over the years with the open standards community. Safe actively participates in the CityGML and INSPIRE communities in Europe. We are also active within the OGC community and participated in many initiatives including test beds, pilots such as Maritime Limits and Boundaries and IndoorGML, and most recently the 2021 Disaster Pilot and 2023 Climate Resilience Pilot. Safe also actively participates in a number of Domain and Standards working groups.
2.4. About Intact
Intact Financial Corporation (IFC) is the largest provider of Property & Casualty (P&C) insurance in Canada. Its purpose is to help people, businesses and society prosper in good times and be resilient in bad times [1]. The company has been on the front lines of climate change with our customers for more than a decade – getting them back on track and helping them adapt. As extreme weather is going to get worse over the next decade, Intact intends to double down on adapting to this changing environment and be better prepared for floods, wildfire and extreme heat [2].
With close to 500 experts in data, artificial intelligence, machine learning and pricing, the Intact Data Lab has deployed almost 300 AI models in production to-date. It is focused on improving risk selection and making operations as efficient as possible while creating outstanding interactions with customers. Within Intact’s Data Lab, the Centre for Climate and Geospatial Analytics (CCGA) uses weather, climate, and geospatial data along with machine learning models and claims data to develop risk maps and other specialized products to the business.
2.5. About Pelagis
Pelagis is an OceanTech venture located in Nova Scotia, Canada. Our foundation focuses on the application of open geospatial technology and standards designed to promote the sustainable use of our ocean resources. As a member of the Open Geospatial Consortium, we co-chair the Marine Domain Working Group responsible for developing a spatially-aware federated service model of marine and coastal ecosystems.
3. Components
The various organizations and institutes that contribute to the Climate Resilience Pilot are described below. There input to the pilot is indicated in the figure below Figure 5.
Figure 5 — CRIS overview
3.1. Component workflow
The figure below shows a high level workflow diagram that illustrates the interactions between data, models and the various components.
Figure 6 — High level workflow diagram that illustrates the interactions between data, models and the various components
4. Raw data to Datacubes
Raw data and Datacubes are two different forms of organizing and structuring data in the context of data analysis and data warehousing.
Raw Data refers to the unprocessed, unorganized, and unstructured data that is collected or generated directly from various sources. It can include a variety of forms such as text, numbers, (geo) images, audio, video, or any other form of data. Raw data often lacks formatting or context and requires further processing or manipulation before it can be effectively analyzed or used for decision-making purposes. Raw data is typically stored in databases or data storage systems.
Datacubes, also known as multidimensional cubes are a structured form of data representation that organizes and aggregates raw data into a multi-dimensional format. Datacubes are designed to facilitate efficient and fast analysis of data from different dimensions or perspectives. They are commonly used in data warehousing.
Datacubes organize data into a multi-dimensional structure typically comprising dimensions, hierarchies, and cells. Dimensions represent various attributes or factors that define the data, such as time, geography, or products. Hierarchies represent the levels of detail within each dimension. Cells typically store the aggregated data values at the intersection of dimensions.
Datacubes enable users to perform complex analytical operations like slicing, dicing, drilling down, or rolling up data across different dimensions. They provide a summarized and pre-aggregated view of data that can significantly speed up query processing and analysis compared to working directly with raw data, something that is very valuable for the climate resilience community. Therefore, Datacubes are often used to support decision-making processes. The example below highlights an climate resilience related example of how to create and make available Datacubes for wildfire risk analysis.
4.1. Using Datacubes for wildfire risk analysis
Ecere is providing a deployment of its GNOSIS Map Server with a focus on a Sentinel-2 Level 2A data cube. OGC API - Tiles, OGC API - Coverages, OGC API - Maps, OGC API — Discrete Global Grid Systems, Common Query Language (CQL2), and OGC API — Processes — Part 3: Workflows & Chaining are the supported standards and extensions for this task.
The plan is to use machine learning process output from the Wildland Fire Fuel Indicator Workflow to identify vegetation fuel types from sentinel-2 bands, then combine with weather data to assess wildfire hazards risk in Australia. The workflow will use as input the sentinel-2 OGC API data cube from our GNOSIS Map Server.
Component: Data Cube and Wildfire vegetation fuel map / risk analysis.
Inputs: ESA Sentinel-2 L2A data (from AWS / Element 84), Temperature / Precipitation / Wind climate data, Reference data for training: vegetation fuel type classification, wildfire risk.
The sentinel-2 Level 2A collection is provided at https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a
Outputs: OGC API (Coverage, Tiles, DGGS, Maps) for Sentinel-2 data (https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a) including full global coverage, all resolutions/scales, all bands that can be individually selected, CQL2 expressions for band arithmetics; climate data (to be added), vegetation fuel type (possibly by end of pilot, or for DP2023), wildfire risk workflow (possibly by end of pilot, or for DP2023).
What other component(s) can interact with the component: Any OGC API client component requiring efficient access to Sentinel-2 data, clients requiring climate data once made available, clients presenting vegetation fuel type, wildfire risk (once ready, might extend into DP2023).
What OGC standards or formats does the component use and produce:
OGC API (Coverage — with subsetting, scaling, range subsetting, coverage tiles; Tiles, DGGS (GNOSISGlobalGrid and ISEA9R), Maps (incl. map tiles), Styles), CQL2, OGC API — Processes with Part 3 for workflows (Nested Local/Remote Processes, Local/Remote Collection Input, Collection Output, Input/Output Field Modifiers)
Formats: GNOSIS Map Tiles (Gridded Coverage, Vector Features, Map imagery, and more); GeoTIFF; PNG (16-bit value single channel for coverage, RGBA for maps); JPEG.
4.1.1. Overview of standards and extensions available for outputs
4.1.1.1. OGC API — DGGS
There are two main requirements classes for this standard.
Data Retrieval (What is here? — ”give me the data for this zone”),
Zones Query (Where is it? — ”which zones match this collection and/or my query”)
Example of data retrieval queries:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/3-4-11/data https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE/data
Figure 7
Example of a zones query:
https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones?f=json (as a list of compact JSON IDs)
Figure 8
Level, Row, Column (which encoded differently in the compact hexadecimal zone IDs) can be seen on the zone information page at:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/3-4-11 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE
Figure 9
There are several different discrete global grids. Two are implemented in our service:
Our GNOSIS Global Grid, which is geographic rather than projected, and is axis-aligned with latitudes and longitudes, but not equal area (though it tends towards equal area — maximum variation is ~48% up to a very detailed level)
ISEA9R, which is a dual DGGS of ISEA3H even levels, using rhombuses/diamonds instead of hexagons, but much simpler to work with and can transport the hexagon area values as points on the rhombus vertices for those ISEA3H even levels. It is also axis-aligned to a CRS defined by rotating and skewing the ISEA projection.
The primary advantage of OGC API — DGGS is:
for retrieving data from DGGS that are not axis-aligned or have geometry that cannot be represented as squares (e.g., hexagons), or
for the zone query capability, most useful for specifying queries (e.g. using CQL2). The extent to which we implement Zones Query at this moment is still limited.
Examples of DGGS Zone information page:
Figure 10 — GNOSIS Map Server information resource for GNOSIS Global Grid zone 5-24-6E
Figure 11 — GNOSIS Map Server information resource for ISEA9Diamonds zone 5-24-6E
Figure 12 — GNOSIS Map Server information resource for ISEA9Diamonds zone 5-24-6E sections
4.1.1.2. OGC API — Coverages with OGC API — Tiles
Because they are axis-aligned, both of these DGGS can be described as a TileMatrixSet, and therefore equivalent functionality to the OGC API — DGGS Data Retrieval requirements class can be achieved using OGC API — Tiles and the corresponding TileMatrixSets instead.
Coverage Tile queries for the same zones:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288
Figure 13
To request a different band than the default RGB (B04, B03, B02) bands:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17?properties=B08 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288?properties=B08
Figure 14
To retrieve coverage tiles with band arithmetic to compute NDVI:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17?properties=(B08/10000-B04/10000)/(B08/10000+B04/10000) https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288?properties=(B08/10000-B04/10000)/(B08/10000+B04/10000)
Figure 15
4.1.1.3. OGC API — Maps with OGC API — Tiles
Map Tiles queries for the same zones:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/GNOSISGlobalGrid/3/4/17 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/ISEA9Diamonds/4/373/288
Figure 16
Figure 17 — GNOSIS Map Server Map of tiles 3/4/17 in GNOSISGlobalGrid
To retrieve a map of the Scene Classification:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/scl/map/tiles/GNOSISGlobalGrid/3/4/17 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/scl/map/tiles/ISEA9Diamonds/4/373/288
Figure 18
Figure 19 — Sentinel-2 with image classification styling
To filter out the clouds:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/GNOSISGlobalGrid/3/4/17?filter=SCL<8 or SCL >10 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/ISEA9Diamonds/4/373/288?filter=SCL<8 or SCL >10
Figure 20
To get an NDVI map:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/ndvi/map/tiles/GNOSISGlobalGrid/3/4/17 https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/ndvi/map/tiles/ISEA9Diamonds/4/373/288
Figure 21
Figure 22 — Sentinel-2 map with NDVI band arithmetic
The same filter= and properties= should also work with the /coverage and /dggs end-points. The filter= also works with the /map end-points.
4.1.2. GNOSIS implementation of OGC API for climate data cube (2016-2025 CMIP5 data)
There is now a fairly complete set of variables from the CMIP5 global dataset (from the Copernicus Climate Data Store) for the 2016-2025 time period available from our GNOSIS data cube implementation at: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5
The variables on a single pressure level are organized as a single collection (coverage / data cube) at: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure (consisting of 9 fields: specific humidity, precipitation, snowfall, sea level pressure, downwelling shortwave radiation, wind speed, mean surface air temperature, maximum daily air temperature, minimum daily air temperature), while the variables on multiple pressure levels are organized into three separate collections: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:temperature https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:gpHeight https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:windSpeed (consisting of two separate fields for Eastward and Northward wind velocity)
The temporal resolution of this dataset is daily, while the source spatial resolution is 2.5 degrees longitude x 2 degrees of latitude, and it is for 8 different pressure levels. Currently, the API supports requesting data from this data using OGC API — Tiles (coverage tiles as well as map tiles), Coverages, Maps and DGGS. With all these APIs, a specific pressure level can be specified for the multi-pressure using e.g., subset=pressure(500), while a specific time can be requested using e.g., datetime=2022-03-01 or subset=time(“2022-03-01”). With Coverages and Maps, a spatial area of interest can be specified using either e.g., bbox=10,20,30,40 or subset=Lat(20:40),Lon(10:30).
At the moment, the Coverages API is limited to 2D output formats (spatial trim, slicing by time and pressure): GeoTIFF and PNG (16-bit output, currently fixed scale: 2.98 and offset: 16384). There is a plan to add support for n-dimensional output formats, including netCDF, CIS JSON and eventually CoverageJSON as well. Currently, separate API requests with the above parameters are needed for different times/pressure levels.
For coverage output, the fields can be selected using properties= (a single field for PNG, and one or more fields for GeoTIFF) e.g., properties=tasmin,tasmax The fields can also be derived using CQL2 expressions that can perform arithmetic e.g., properties=pr*1000.
With all these APIs, it is also possible to filter fields with filter= also specified as a CQL2 expression e.g., filter=tasmax>300 (unmatched cells will be replaced by NODATA values). The domains of the collections are described in the collection description (inside the extent property) as well as in the Coverages CIS DomainSet resource e.g., https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure?f=json , https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/domainset?f=json
The ranges of the collections are described in the Coverages CIS RangeType resource as per the example below, and we are also planning to implement describing in a /schema resource that will be harmonized with the OGC API — Features schema. https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/rangetype?f=json
Some sample requests: Maps
Proper symbolization here will require support for wind barbs — in the meantime the Eastward and Northward velocity are assigned to the green and blue color channels.
Tiles
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/tiles/WebMercatorQuad/1/1/0?f=geotiff=2022-09-04 (GeoTIFF Coverage Tile)
DGGS
Data retrieval — What is here? (equivalent to Coverage Tiles requests for DGGSs whose zone geometry can be described by a 2D Tile Matrix Set e.g., GNOSISGlobalGrid, ISEA9R, rHealPix):
Zones query — Where is it?: Where is maximum daily temperature greater than 300 degrees Kelvins on September 4, 2022? (at precision level of GNOSIS Global Grid level 6)
Figure 23 — GeoJSON output
(Plain JSON Zone ID list output)
(Binary 64-bit integer Zone IDs)
(GeoTIFF output) (using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)
By creating a kind of mask at a specifically requested resolution level, DGGS Zones Query can potentially greatly help parallelization and orchestration of spatial queries combining multiple datasets across multiple services, allowing to perform early optimizations with lazy evaluation.
Coverages
(GeoTIFF coverage with 5 bands for each field)
As a test of higher resolution data, we also loaded an hourly dataset for the ERA5 relative humidity for the April 1-6, 2023 period at: https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity
The spatial resolution for this one is also higher at 0.25 degrees longitude x 0.25 degrees latitude, and the data is for 37 different pressure levels. Some sample requests:
Maps
Tiles
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage/tiles/WorldCRS84Quad/0/0/0?f=geotiff=pressure(750) (GeoTIFF coverage tile)
Coverages
(GeoTIFF Coverage)
DGGS
Data retrieval — What is here? (equivalent to Coverage Tiles requests for DGGSs whose zone geometry can be described by a 2D Tile Matrix Set e.g., GNOSISGlobalGrid, ISEA9R, rHealPix):
Zones query — Where is it?: Where is relative humidity at 850 hPa greater than 80% on April 3rd, 2023? (at precision level of GNOSIS Global Grid level 6) https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=geojson
Figure 24 — GeoJSON output
(Plain Zone ID list output)
(Binary 64-bit integer Zone IDs)
(GeoTIFF output) (using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)
We hope that our API and these climate datasets proves useful to other participants and can be part of Technology Integration Experiments for the pilots and/or Testbed 19 GeoDataCube.
We have also been working on our client to visualize these data sources from local netCDF files, our native GNOSIS data store, or remotely through OGC APIs, and we are working on support for EDR in order to perform integration experiments with the NOAA EDR API.
Figure 25 — GeoJSON output
We are also planning work on demonstrating the integration of these datasets as cross-collection queries and with our OGC API — Processes implementation including support for Part 3 — Workflows and Chaining.
One process we are putting together is a machine learning prediction process for classifying fuel vegetation types, based on sentinel-2 Level 2A accessed through our API at:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a
The initial training data will be using this Fuel vegetation Type coverage for the whole continental US from landfire.gov available from our API at:
https://maps.gnosis.earth/ogcapi/collections/wildfire:USFuelVegetationTypes
More work is being done on loading additional fire danger indices from the Copernicus Climate Data Store.
5. Raw data to Analysis Ready Data (ARD)
CEOS defines Analysis Ready Data as satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. See https://ceos.org/ard/, and especially the information for data producers: https://ceos.org/ard/files/CARD4L_Info_Note_Producers_v1.0.pdf.
5.1. Transforming climate relevant raw data to ARD
Several past successful OGC testbeds, including the DP 21 to which this pilot is linked, have looked at ARD and IRD but also in terms of use cases. In this pilot, some main technical contributions have been creating digestible OGC data types and formats for specific partner use cases, so producing ARD from publically available EO and model data, including hydrological and other type of model output as well as climate projections.
These ARD will feed into all use cases for all participants, with a particular focus toward the use cases proposed for Heat, Drought and Health Impacts by participants in the pilot.
Specifically, participants provide access to the following satellite and climate projection data:
Wildfire: Fire Radiant Power (FRP) product from Sentinel 3 (NetCDF), 5p, MODIS products (fire detection), VIIRS (NOAA); possibly biomass availability (fire fuel).
Land Surface Temp — Sentinel 3
Pollution — Sentinel 5p
Climate Projection data (NetCDF, etc., daily downscaled possible): air temp (10 m above ground). Rainfall and possibly wind direction as well
Satellite-derived Discharge Data to look at Droughts/Floods etc. by basin or other scale
Hydrological model simulation outputs at (sub)basin scale (within reason)
The created ARD in various OGC interoperable formats created digestible dataflows for the proposed OGC Use Cases. This proposed data chain by several participants is similar to DP21, in which contributors, like RSS-Hydro, SafeSoftware, and others also participated. A generated climate indicator or EO remotely sensed data (NASA, NOAA, ESA, etc.) from various sources are “simplified”to GeoTIFF and / or vectorized geopackage per time step by other participants’ tools, such as the FME software (by SafeSoftware). Another option as an intermediate data type (IRD) can be COG — cloud optimized geotiff which would make access more efficient. The COG GeoTIFFs are optimized for cloud so data sharing can be made more efficient. ARD and IRD should become more service / cloud based wherever possible.
Besides the data format, data structures and semantics required to support the desired DRI’s are important. The time series / raster, and classification to vector contour transform is an approach that worked well in DP21 and has been a good starting point also in this pilot. For example, together in the FME processing engine, time series grids can be aggregated across timesteps to mean or max values, then classify them into ranges suitable for decision making, and then write them out and expose them as time tagged vector contour tables.
In summary, the different ARD and IRD data can be created from the following data sources:
Inputs: EO (US sources fire related: MODIS, VIIRS); Climate projections, sub catchment polygons, ESA sources; Sentinel-3, Sentinel 5-P.
Outputs forma & instances: WCS, GeoTIFF spatial / temporal subset, Shape; NetCDF.
Output parameters: e.g. hydrological condition of a basin (historically/current). So drought / flood etc.
Output themes: downscaled / subset outputs, hydrologic scenarios.
Another highly relevant input are the Essential Climate Variables (ECV) Inventory (https://climatemonitoring.info/ecvinventory/) houses information on Climate Data Records (CDR) provided mostly by CEOS and CGMS member agencies. The inventory is a structured repository for the characteristics of two types of GCOS ECV CDRs:
Climate data records that exist and are accessible, including frequently updated interim CDRs; Climate data records that are planned to be delivered.
Figure 26
The ECV Inventory is an open resource to explore existing and planned data records from space agency sponsored activities and provides a unique source of information on CDRs available internationally. Access links to the data are provided within the inventory, alongside details of the data’s provenance, integrity and application to climate monitoring.
Participants, particularly GMU CSISS have demonstrated the use of ECV record information as input with OpenSearch service endpoint (currently CMR(CWIC) and FedEO), and downloading URLs for accessing NetCDF or HDF files.
Outputs in this case include WCS service endpoint for accessing selected granule level product images (GeoTIFF, PNG, JPEG, etc.), focusing on the WCS for downloading images and WMS for showing layers on a basemap.
5.3. From Raw Data to ARD with the FME Platform
5.3.1. Component Descriptions
D100 — Client instance: Analysis Ready Data Component
Our Analysis Ready Data component (ARD) uses the FME platform to consume regional climate model and EO data and generate FAIR datasets for downstream analysis and decision support.
The challenge to manage and mitigate the effects of climate change poses difficulties for spatial and temporal data integration. One of the biggest gaps to date has been the challenge of translating the outputs of global climate models into specific impacts at the local level. FME is ideally suited to help explore options for bridging this gap given its ability to read datasets produced by climate models such as NetCDF or OGC WCS and then filter, aggregate, interpolate and restructure it as needed. FME can inter-relate it with higher resolution local data, and then output it to whatever format or service is most appropriate for a given application domain or user community.
Our ARD component supports the consumption of climate model outputs such as NetCDF. It also has the capacity to consume earth observation (EO) data, and the base map datasets necessary for downstream workflows, though given time and resource constraints during this phase we did not pursue consumption of other data types besides climate data.
5.3.1.1. ARD Workflow
The basic workflow for generating output from the FME ARD component is as follows. The component extracts, filters, interrelates and refines these datasets according to indicator requirements. After extraction, datasets are filtered by location and transformed to an appropriate resolution and CRS. Then the workflow resamples, simplifies and reprojects the data, and then defines record level feature identifiers, ECV values, metadata and other properties to satisfy the target ARD requirements. This workflow is somewhat similar to what was needed to evaluate disaster impacts in DP21. Time ranges for climate scenarios are significantly longer — years rather than weeks for floods.
Once the climate model, and other supporting datasets have been adequately extracted, prepared and integrated, the final step is to generate the data streams and datasets required by downstream components and clients. The FME platform is well suited to deliver data in formats as needed. This includes Geopackage format for offline use. For online access, other open standards data streams are available, such as GeoJSON, KML or GML, via WFS and OGC Features APIs and other open APIs. For this pilot we generated OGC Geopackage, GeoJSON, CSV and OGC Features API services.
Figure 32 — High level FME ARD workflow showing generation of climate scenario ARD and impacts from climate model, EO, IoT, infrastructure and base map inputs
As our understanding of end user requirements continues to evolve, this will necessitate changes in which data sources are selected and how they are refined, using a model based rapid prototyping approach. We anticipate that any operational system will need to support a growing range of climate change impacts and related domains. Tools and processes must be able to absorb and integrate new datasets into existing workflows with relative ease. As the pilot develops, data volumes increase, requiring scalability methods to maintain performance and avoid overloading downstream components. Cloud based processing near cloud data sources using OGC API web services supports data scaling. Regarding the FME platform, this involves deployment of FME workflows to FME Cloud. Note that in future phases, we are likely to test how cloud native datasets (COG, STAC, ZARR) and caching can be used to scale performance as data transactions and volume requirements increase.
It is worth underlining that our ARD component depends on the appropriate data sources in order to produce the appropriate decision ready data (DRI) for downstream components. Risk factors include being able to locate and access suitable climate models of sufficient quality, resolution and timeliness to support indicators as the requirements and business rules associated with them evolve. Any data gaps encountered are documented under this section under Challenges and Opportunities and in the common Lessons Learned chapter and the end of the ER.
Figure 33 — Environment Canada NetCDF GCM time series downscaled to Vancouver area. From: https://climate-change.canada.ca/climate-data/#/downscaled-data
Figure 34 — Data Cube to ARD: NetCDF to KML, Geopackage, GeoTIFF
Original Data workflow: - Split data cube - Set timestep parameters - Compute timestep stats by band - Compute time range stats by cell - Classify by cell value range - Convert grids to vector contours
Figure 35 — Extracted timestep grids: Monthly timesteps, period mean T, period max T
Figure 36 — Convert raster temperature grids into temperature contour areas by class
Figure 37 — Geopackage Vector Area Time Series: Max Yearly Temp
5.3.1.2. ARD Development Observations
Figure 38 — FME Data Inspector: RCM NetCDF data cube for Manitoba temperature 2020-2099
Disaster Pilot 2021 laid a good foundation for exploring data cube extraction and conversion to ARD with using the FME data integration platform. A variety of approaches were explored for extraction, simplification and transformation including approaches to select, split, aggregate, and summarize time series. However, more experimentation was needed to generate ARD that can be queried to answer questions about climate trends. This evolution of ARD was one of the goals for this CRP. This goal includes better support for both basic queries, and analytics, statistical methods, simplification & publication methods, including cloud native — NetCDF to Geopackage, GeoJSON and OGC, APIs.
In consultation with other participants, we learned fairly early on in the pilot that our approach to temperature and precipitation contours or polygons inherited from our work in DP21 on flood contours involved too much data simplification to be useful. For example, contouring required grid classification into segments, such as 5 degree C or 10mm of precipitation etc. However, this effective loss of detail oversimplified the data to the point where it no longer held enough variation over local areas to be useful. In discussion with other participants, it was determined that simply converting multidimensional data cubes to vector time series point data served the purpose of simplifying the data structure for ease of access, but retained the ECV precision needed to support a wider range of data interpretations for indicator derivation. It also meant that as a data provider we did not need to anticipate or encode interpretation of indicator business rules into our data simplification process. By simply providing ECV data points, the end user was free to run queries to find locations and time steps where temp > or precipitation < some threshold of interest.
Initially it was thought that classification rules need to more closely model impacts of interest. For example, the business rules for a heat wave might use a temperature range and stat type as part of the classification process before conversion to vector. However, this imposes the burden of domain knowledge on the data provider rather than on the climate service end user who is much more likely to understand the domain they wish to apply the data to and how best to interpret it.
Modified ARD Data workflow: - Split data cube - Set timestep parameters - Compute timestep stats by band - Compute time range stats by cell - Convert grids to vector contours
Further scenario tests were explored, including comparison with historical norms. Calculations were made using the difference between projected climate variables and historical climate variables. These climate variable deltas may well serve as a useful starting point for climate change risk indicator development. They also serve as an approach for normalizing climate impacts when the absolute units are not the main focus. Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.
Figure 39 — Modified ARD Worflow: NetCDF data cube to precipitation delta grids (future - historical) in Geopackage for LA
ARD Climate Variable Delta Data workflow: - Split data cubes from historic and future netcdf inputs - Set timestep parameters - Compute historic mean for 1950 — 1980 per month based on historic time series input - Multiply historic mean by -1 - Use RasterMosaiker to sum all future grids with -1 * historic mean grid for that month - Normalize environmental variable difference by dividing by historic range for that month delta / (max — min) - Convert grids to vector contours - Define monthly environment variables from band and range values
More analysis needs to be done with higher resolution time steps — weekly and daily. At the outset monthly time steps were used to make it easier to prototype workflows. Daily time step computations will take significantly more processing time. Future pilots should explore ways of better supporting scalability of processing through automation and cloud computing approaches such as the use of cloud native formats (STAC, COG, ZARR etc).
5.3.1.3. OGC API Features Service
Compared to OGC WFS2, OGC APIs are a simpler and more modern standard based on a REST and JSON / openAPI approach. However we found implementation of OGC API services somewhat challenging. There seems to be more complexity in terms of number of ways for requesting features, and too many options for representing service descriptions. As every client tends to interpret and use the standard a bit differently — it becomes a challenge to derive how to configure service for a wide range of clients. In particular, QGIS / ArcPro were a challenge to debug given limited logging. For QGIS, we had to examine cache files in the operating system temp directories to look for and resolve errors.
Once correctly configured, OGC API feature services seemed to perform well and likely are more efficient than the equivalent WFS2 / GML feature services. A key aspect of performance improvement was achieving query parameter continuity by passing query settings from the client all the way to the database reader configuration. For example, it was important to make sure the spatial extent and feature limits from the end user client were implemented in the database SQL extraction query and not just at an intermediate stage. We will need to explore better use of caching to further optimize performance. There may also be opportunities for pyramiding time series vector data at a lower resolution for wide area requests. This may better serve those interested in observing large area patterns who don’t necessarily need full resolution at the local level.
It should also be noted that while OGC API services should be a priority for standards support, for a climate and disaster management context, given the relative recent nature of these standards many users may be less than familiar with or prepared to use these standards. As such, there should also be provision to access data directly in well accepted open standards such as GeoJSON, CSV, GeoTIFF, Geopackage or Shape. In this project, some users preferred direct access to GeoJSON or CSV over OGC API access.
5.4. A framework example for climate ARD generation
Wuhan University (WHU) is a university that plays a significant role in researching and teaching all aspects of surveying and mapping, remote sensing, photogrammetry, and geospatial information sciences in China. In this Climate Resilience Pilot, WHU will contribute three components (ARD, Drought Indicator, and Data Cube) and one use-case (Drought Impact Use-cases).
5.4.1. Component: ARD
Inputs: Gaofen L1A data and Sentinel-2 L1C data
Outputs: Surface Reflectance ARD
What other component(s) can interact with the component: Any components requiring access to surface reflectance data
Surface Reflectance (SR) is the fraction of incoming solar radiation reflected from the Earth’s surface for specific incidents or viewing cases. It can be used to detect the distribution and change of ground objects by leveraging the derived spectral, geometric, and textural features. Since a large amount of optical EO data has been released to the public, ARD can facilitate interoperability through time and multi-source datasets. As the probably most widely applied ARD product type, the SR ARD can contribute to climate resilience research. For example, the SR-derived NDVI series can be applied to monitor wildfire recovery by analyzing vegetation index increases. Several SR datasets have been assessed as ARD by CEOS, like the prestigious Landsat Collection 2 Level 2, and Sentinel-2 L2A, while many other datasets are still provided at a low processing level.
WHU is developing a pre-processing framework for SR ARD generation. The framework supports radiometric calibration, geometric ratification, atmospheric correction, and cloud mask. To address the inconsistencies in observations from different platforms, including variations in band settings and viewing angles, we proposed a processing chain to produce harmonized ARD. This will enable us to generate SR ARD with consistent radiometric and geometric characteristics from multi-sensor data, resulting in improved temporal coverage. In the first stage of our mission, we are focusing on the harmonization of Chinese Gaofen data and Sentinel-2 data, as shown in Figure 1, the harmonization involves spatial co-registration, band conversion, and bidirectional reflectance distribution function (BRDF) correction. Figure 2 shows the Sentinel-2 data before and after pre-processing. Furthermore, we wish to seek the assessment of CEOS-ARD in our long-term plan.
Figure 40 — The processing chain to produce harmonized ARD.
Figure 41 — Sentinel-2 RBG composite (red Band4, green Band3, blue Band2), over Hubei, acquired on October 22, 2020. (a) corresponds to the reflectance at the top of the atmosphere (L1C product); (b) corresponds to the surface reflectance after pre-processing.
5.4.2. Component: Drought Indicator
Inputs: Climate data, including precipitation and temperature
Outputs: Drought risk map derived from drought indicator
What other component(s) can interact with the component: Any components requiring access to drought risk map through OGC API
What OGC standards or formats does the component use and produce: OGC API — Processes
Drought is a disaster whose onset, end, and extent are difficult to detect. Original meteorological data, such as precipitation, can be obtained through satellites and radar, which can be used for drought monitoring. However, the accuracy is easily affected by detection instruments and terrain occlusion, and the ability to retrieve special shapes, such as solid precipitation, is limited. In addition, many meteorological monitoring stations on the ground can provide local raw meteorological observation data. The SPEI is a model to monitor, quantitatively analyze, and determine the spatiotemporal range of the occurrence of drought using meteorological observation data from various regions. It should supplement the result of drought monitoring with satellite and radar.
SPEI has two main characteristics: 1) it considers the deficits between precipitation and evapotranspiration comprehensively, that is, the balance of water; 2) multi-time scale characteristics. For 1) drought is caused by insufficient water resources. Precipitation can increase water, while evapotranspiration can reduce water. The differences between the two variables simultaneously and in space can characterize the balance of water. For 2), the deficits value of different usable water sources is distinct at different time scales due to the different evolution cycles of different types, resulting in various representations in temporal. By accumulating the difference between precipitation and evapotranspiration at different time scales, agricultural (soil moisture) droughts, hydrological (groundwater, streamflow, and reservoir) droughts, and other droughts can be distinguished by SPEI.
In our project, the dataset for SPEI calculation is ERA5-Land monthly averaged data from 1950 to the present. We selected years of data about partial areas of East Asia for experiments. Through the following flow of the SPEI calculation, we obtain the SPEI value for assessments of drought impact. The flow of the SPEI calculation is shown in Figure 3.
Figure 42 — Flow of the SPEI calculation.
WHU has provided the SPEI drought index calculation services through the OGC API — Processes, enabling interaction with other components. The current endpoint for OGC API — Processes is http://oge.whu.edu.cn/ogcapi/processes_api. This section will explain how to use this API for calculating the drought index.
Example:/processes http://oge.whu.edu.cn/ogcapi/processes_api/processes The API endpoint for retrieving the processes list.
Example:/processes/{processId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei The API endpoint for retrieving a process description (e.g. spei). This returns the description of “spei” process, which contains the inputs and outputs information.
Example:/processes/{processId}/execution http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/execution The API endpoint for executing the process. The spei process exclusively supports asynchronous execution, resulting in the creation of a job for processing. The request body:
{ “inputs”: { “startTime”: “2010-01-01”, “endTime”: “2020-01-01”, “timeScale”: 5, “extent”: { “bbox”: [73.95, 17.95, 135.05, 54.05], “crs”: “http://www.opengis.net/def/crs/OGC/1.3/CRS84” } } }
Example:/processes/{processId}/jobs/{jobId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId} The API endpoint for retrieving status of a job.
Example:/processes/{processId}/jobs/{jobId}/results http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId}/results The API endpoint for retrieving the results of a job, which are encoded as : [{ “value”: { “time”: “2000_02_01”, “url”: “http://oge.whu.edu.cn/api/oge-python/data/temp/9BC500C1B0E3438C090AF5C6F8602045/8d0357fb-8ffb-4e62-9c3a-55ad17a5831a/SPEI_2000_02_01.png” } }, …… ]
Figure 43 — The SPEI results for the date 2000_02_01.
5.4.3. Component: Data Cube
Inputs: ERA5 temperature and precipitation data
Outputs: Results in the form of GeoTIFF after processing in Data Cubes
What other component(s) can interact with the component: Any components requiring access to temperature and precipitation data in part of Asia through OGC API
What OGC standards or formats does the component use and produce: OGC API- Coverages
WHU has introduced GeoCube as a cube infrastructure for the management and large-scale analysis of multi-source data. GeoCube leverages the latest generation of OGC standard service interfaces, including OGC API-Coverages, OGC API-Features, and OGC API-Processes, to offer services encompassing data discovery, access, and processing of diverse data sources. The UML model of the GeoCube is given in Figure 5, and it has four dimensions: product, spatial, temporal, and band. Product dimension specifies the thematic axis for the geospatial data cube using the product name (e.g. ERA5_Precipitation or OSM_Water), type (e.g. raster, vector, or tabular), processes, and instrument name. For example, the product dimension can describe optical image products by recording information on the instrument and band. Spatial dimension specifies the spatial axis for the geospatial data cube using the grid code, grid type, city name, and province name. The cube uses a spatial grid for tiling to enable data readiness in a high-performance form. Temporal dimension specifies the temporal axis for the geospatial data using the phenomenon time and result time. Band dimension describes the band attribute of the raster products according to the band name, polarization mode that is reserved for SAR images, and product-level band. The product-level band is the information that is extracted from the original bands. For example, the Standardized Precipitation Evapotranspiration Index (SPEI) band is a product-level band that takes into account the hydrological process and evaluates the degree of drought by calculating the balance of precipitation and evaporation.
Figure 44 — The UML model of WHU Data Cube.
WHU has organized ERA5 temperature and precipitation data into a cube and offers climate data services through the OGC API — Coverages, supporting the computation of various climate indices. The API endpoint is http://oge.whu.edu.cn/ogcapi/coverages_api, allowing users to query and retrieve the desired data from the cube. This section provides examples demonstrating how to access the data from the cube using OGC API — Coverages.
Example:/collections http://oge.whu.edu.cn/ogcapi/coverages_api/collections?bbox=112.65942,29.23223,115.06959,31.36234=10=2016-01-01T02:55:50Z/2018-01-01T02:55:50Z The API endpoint for querying datasets from the cube, and the query parameters including limit, bbox, and time.
Example:/collections/{collectionId} http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602 The API endpoint for retrieving the description of the coverage with the specified ID from the cube.
Example:/collections/{collectionId}/coverage http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage The API endpoint for retrieving the coverage in GeoTIFF format for the specified ID. Here is an example of the response:
Figure 45 — The coverage with the ID "2m_temperature_201602" in the Asian region.
Example:/collections/{collectionId}/coverage/rangetype http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/rangetype The API endpoint for accessing the range type of the coverage, which is part of the band dimension members in the cube. In this example, the coverage consists of only one band dimension member.
Example:/collections/{collectionId}/coverage/domainset http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/domainset The API endpoint for the domain set of the coverage, which is also the domain set of the cube.
5.5. ESRI Climate Resilience Data
5.5.1. Climate Projection Data
To make climate projection data more easily usable we transformed CMIP5 data (version 1 of our project), now working on CMIP6, into an Analysis Ready Data collection of indices of future temperature and precipitation. Climate summaries for the contiguous 48 states were derived from data generated for the 4th National Climate Assessment. These data were accessed from the Scenarios for the National Climate Assessment website. The 30-year mean values for 4 time periods (historic, early-, mid-, and late-century) and two climate scenarios (RCP 4.5 and 8.5) were derived from the Localized Constructed Analogs (LOCA) downscaled climate model ensembles, processed by the Technical Support Unit at NOAA’s National Center for Environmental Information.
Historical: 1976-2005
Early-Century: 2016-2045
Mid-Century: 2036-2065
Late-Century: 2070-2099
In order to display the full range of projections from individual climate models for each period, data originally obtained from USGS THREDDS servers were accessed via the Regional Climate Center’s Applied Climate Information System (ACIS). This webservice facilitated processing of the raw data values to obtain the climate hazard metrics available in CMRA.
As LOCA was only generated for the contiguous 48 states (and the District of Columbia), alternatives were used for Alaska and Hawaii. In Alaska, the Bias Corrected Spatially Downscaled (BCSD) method was used. Data were accessed from USGS THREDDS servers. The same variables provided for LOCA were calculated from BCSD ensemble means. However, only RCP 8.5 was available. Minimum, maximum, and mean values for county and census tracts were calculated in the same way as above. For Hawaii, statistics for two summary geographies were accessed from the U.S. Climate Resilience Toolkit’s Climate Explorer: Northern Islands (Honolulu County, Kauaʻi County) and Southern Islands (Maui County, Hawai’i County).
This data is being updated to CMIP6 and will be available in the latter half of 2023. The system is being expanded globally using NASA NEX CMIP6 data using the same time periods and climate scenarios.
5.5.2. Climate Indices
To provide a more approachable context to future climate, a collection of 47 indices of future temperature and precipitation are computed. These indices build upon prior work on Climdex indices and additional indices developed for National Climate Assessment 4 (NCA4).
Cooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]
Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)
Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)
Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)
First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)
Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]
Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]
Growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)
Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)
Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)
Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]
Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)
Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]
Precip Annual Total: Annual total precipitation [inches]
Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]
Precip 1in: Annual number of days with total precipitation greater than 1 inch
Precip 2in: Annual number of days with total precipitation greater than 2 inches
Precip 3in: Annual number of days with total precipitation greater than 3 inches
Precip 4in: Annual number of days with total precipitation greater than 4 inches
Precip Max 1 Day: Annual highest precipitation total for a single day [inches]
Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]
Daily Avg Temperature: Daily average temperature [degF]
Daily Max Temperature: Daily maximum temperature [degF]
Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005
Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005
Days Above 100F: Annual number of days with a maximum temperature greater than 100degF
Days Above 105F: Annual number of days with a maximum temperature greater than 105degF
Days Above 110F: Annual number of days with a maximum temperature greater than 110degF
Days Above 115F: Annual number of days with a maximum temperature greater than 115degF
Temp Max 1 Day: Annual single highest maximum temperature [degF]
Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)
Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]
Days Above 86F: Annual number of days with a maximum temperature greater than 86degF
Days Above 90F: Annual number of days with a maximum temperature greater than 90degF
Days Above 95F: Annual number of days with a maximum temperature greater than 95degF
Temp Min: Daily minimum temperature [degF]
Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degF
Temp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degF
Temp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degF
Temp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degF
Temp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005
Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005
Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degF
Temp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]
Temp Min 1 Day: Annual single lowest minimum temperature [degF]
Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)
Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]
The individual web services of climate indices and raster data for download can be accessed at: https://resilience.climate.gov/pages/climate-model-content-gallery
Or for each scenario:
Historical: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-historical/about
RCP 4.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-early-century/about
RCP 4.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-mid-century/explore?location=34.597533%2C-95.830000%2C5.00
RCP 4.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-late-century/about
RCP 8.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-early-century/about
RCP 8.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-mid-century/about
RCP 8.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-late-century/explore?location=34.561983%2C-95.830000%2C5.00
The data can be viewed directly in the online map viewer or opened in ArcGIS Online, ArcGIS Desktop, or a StoryMap. To view in other softwares GeoService and KMZ URLs are on the right side of the page under View API Resources.
Figure 46 — View API Resources
5.5.3. Summarized Indices for Locations
To support easier interpretation and local decision making, the above indices were summarized by county, census tract, and tribal areas using the Zonal Statistics as Table utility in ArcGIS Pro. The results were joined into the corresponding geography polygons. A minimum, maximum, and mean value for each variable was calculated. This process was repeated for each time range and scenario. Precomputing enables quick map and graph response in the web application, and also provides as easily reusable download for someone who wants to utilize the data elsewhere.
To reuse the summarized services outside of the CRMA application or to download the processed data visit the links below for the geography of interest.
American Indian/Alaska Native/Native Hawaiian Areas: https://resilience.climate.gov/datasets/nationalclimate::climate-mapping-resilience-and-adaptation-cmra-climate-assessment-data/explore?layer=2=-0.000000%2C0.000000%2C2.71
On these pages, a list of buttons allow you to filter the selection to a subset by attribute or geography, download into a variety of formats, and translate the descriptive documentation for viewing in other languages.
6. ARD to Decision Ready Indicator (DRI)
A decision Ready Indicator (DRI) is information and knowledge that is in such a format that it provides specific support for actions and decisions that have to be made. These indicators are pre-determined, using a set recipe which pulls together one or more ARDs to create an indicator of action and/or decision. DRIs hold significant importance as they serve as benchmarks or criteria to determine when a decision-making process is adequately prepared and can proceed efficiently. Their importance lies in several aspects. Firstly, DRIs facilitate efficient decision-making by signaling that all necessary information, analysis, and resources are available, minimizing delays and preventing hasty or uninformed decisions. Secondly, they ensure quality assurance by setting standards for the decision-making process, ensuring thorough consideration of relevant factors, accurate analysis, and reliable information. DRIs also promote accountability and transparency by defining expectations and providing a framework for evaluation, enabling stakeholders to understand the reasoning behind decisions and hold decision-makers accountable. Additionally, DRIs aid in effective resource allocation by identifying the point at which resources can be allocated, preventing wastage on underprepared decisions. They also assist in managing risks associated with decision-making by encouraging thorough analysis and consideration of potential risks. Furthermore, DRIs promote consistency and standardization, reducing subjectivity and increasing fairness across different decisions. In summary, DRIs play a crucial role in ensuring well-prepared, informed, and accountable decision-making processes, enhancing efficiency, quality, transparency, and resource management.
Analyze Ready Data (ARD), data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets, form the building blocks for DRIs. The transition from ARDs to DRIs encompasses a series of steps designed to extract meaningful insights and facilitate informed decision-making. It commences with the collection and preparation of data, where relevant information is gathered from diverse sources and formatted appropriately for analysis. This involves data cleaning, standardization, and transformation to ensure consistency and reliability. Following data preparation, the integration stage merges multiple data sources, aligning them based on common variables or identifiers, thereby creating a comprehensive dataset.
Subsequently, data exploration and analysis techniques are employed to delve into the dataset’s intricacies. Through statistical analysis, data visualization, and data mining, analysts uncover patterns, relationships, and trends that enable a deeper understanding of the underlying information. Feature engineering plays a crucial role in enhancing the analytical model’s performance. By selecting pertinent features, transforming existing variables, handling missing data, and encoding categorical variables, analysts optimize the model’s ability to extract insights from the data.
Once the data is prepared and features are engineered, model development ensues. Depending on the nature of the problem and the data at hand, analysts choose appropriate algorithms, such as regression, classification, clustering, or machine learning, to build predictive or analytical models. These models are then trained using a portion of the data, often referred to as the training set. Validation is performed using a separate portion of the data, the validation set, to assess the model’s performance and fine-tune it for optimal results.
With the validated model in place, the focus shifts to generating Decision Ready Indicators (DRIs). These indicators are specific metrics, scores, or predictions derived from the model’s outputs, providing actionable insights relevant to the decision-making process. The DRIs serve as valuable tools that support decision-makers in interpreting the analyzed data and guide them in making well-informed choices.
The generated DRIs become pivotal components in the decision-making process. Decision-makers leverage these indicators to assess different scenarios, evaluate risks, and identify opportunities. By incorporating the insights gained from the analyzed data and model outputs, decision-makers can make more informed and data-driven decisions, enhancing their ability to achieve desired outcomes.
It is worth noting that while the outlined steps provide a general framework, the specific implementation of the process may vary based on the unique context, data characteristics, and analytical techniques employed. Nonetheless, the overarching objective remains constant: to transform Analyze Ready Data into Decision Ready Indicators that facilitate effective decision-making. Below we provide examples on what DRIs can be developed in relation to Climate Resilience.
6.1. Wildfire hazard component
To develop its component, Intact migrated its previous proprietary wildfire hazard model to a private on-premise data science environment. For key inputs to the model, external connections to several open data repositories were established. To facilitate these access tests, several public open source datasets, such as climate model outputs, Earth observations, weather and geospatial, were vetted by the appropriate cybersecurity boards. The tests also informed experts of changes in platforms offerings, of new data products specifications, applicable licenses, and of current authoritative scientific references.
Figure 48 — Two samples of IFC’s current national wildfire hazard map
The table below shows the datasets accessed by Intact during the pilot.
6.1.1. Technical Interoperability Experiments (TIE) Table
| Dataset | Source | URL | Notes |
|---|---|---|---|
| National Fire Database fire polygon data | NRCan | https://cwfis.cfs.nrcan.gc.ca/datamart/download/nfdbpoly | Unable to establish SSL connection into private network |
| Fire Weather Index and its components | NRCan | https://cwfis.cfs.nrcan.gc.ca/downloads/fwi_obs/ | Unable to establish SSL connection into private network |
| Forest Fuels | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/fuels/ | |
| Vegetation concentration and mass | NRCan | http://tree.pfc.forestry.ca/ | 503 Service Unavailable from private network |
| Daily reanalysis composites | NOAA | https://psl.noaa.gov/data/composites/day/ | |
| Monthly reanalysis composites | NOAA | https://psl.noaa.gov/cgi-bin/data/composites/printpage.pl | |
| Global temperature anomalies/trends | NASA | https://data.giss.nasa.gov/gistemp/maps/ | |
| Elevation at 30 meters | NASA | https://lpdaac.usgs.gov/products/nasadem_hgtv001/ | |
| Canadian Drought Monitor | AAFC | https://agriculture.canada.ca/atlas/data_donnees/canadianDroughtMonitor/data_donnees/shp/ | |
| Canadian Lightning Detection Network | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/CLDN/ | Connection timed out, can’t find alternate source |
| Topography | USGS | https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm | Interactive map, not layers |
| Road segments | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/base_data | Connection timed out, can’t find alternate source |
| Population of the world | Columbia U. | https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density/data-download | |
| CanVec Manmade Structures | NRCan | http://ftp.geogratis.gc.ca/pub/nrcan_rncan/vector/canvec/shp/ManMade/ | 503 Service Unavailable from private network |
Below is a summarized list of the key datasets required to produce or update a wildfire hazard map.
National fire database polygon data
Fire Weather Index (FWI) daily maps
Land cover maps
Drought conditions
Digital Elevation Model (DEM)
Population density
Fuel and vegetation data
Intact’s wildfire hazard map is developed exclusively for internal use. Aside from intellectual property terms, it is meant to be deployed in highly secured data environments, and as such it cannot readily interact with other components of the pilot at this point of time. The intent is to develop geospatial infrastructures and legal terms that would allow a closer collaboration with pilot’s participants.
Very early in the project, Intact also developed an H3 synthetic exposure dataset (see next figure) composed of 14M points spread out across the country in a statically representative pattern. The purpose of this dataset was to facilitate visualization and analysis of the exposure. It was also supposed to allow pilot participants to have a common exposure reference on which to develop decision-ready use cases for insurance, thus advancing towards standardization. Unfortunately, time constraints prevented update and sharing of this dataset.
Figure 49 — IFC’s exposure synthetic dataset, with Montreal – Ottawa corridor on the left, and close-up of Montreal on the right. Color scale represent relative risk density in each cell, while points are representative individual risks
6.2. The Blue Economy
Pelagis’ participation in the Climate Resilience pilot focuses on enhancing our view of a global oceans observation system by combining real-world ground observations with analysis ready datasets. Monitoring aspects of our oceans through both a temporal and spatial continuum while providing traceability through the observations process allows stakeholders to better understand the stressors affecting the health of our oceans and investigate opportunities to mitigate the longer term implications related to climate change.
The approach to address the needs for a sustainable ocean economy is to make Marine Spatial Planning a core foundation on which to build out vertical applications. Pelagis’ platform is based on a federated information model represented as a unified social graph. This provides a decentralized approach towards designing various data streams each represented by their well-known and/or standardized model. To date, service layers based on the OGC standards for Feature, Observations & Measurements, and Sensors APIs have been developed and extended for adoption within the marine domain model. Previous work provides for data discovery and processing of features based on the IHO S-100 standard (Marine Protected Areas, Marine Traffic Management, …); NOAA open data pipelines for major weather events (Hurricane Tracking, Ocean Drifters, Saildrones …); as well as connected observation systems as provided by IOOS and its Canadian variant, CIOOS.
6.2.1. From Raw Data to ARD and Decision Ready Indicators
The United Nations Framework Convention on Climate Change (UNFCCC) is supported through a number of organizations providing key observation data related to climate change. Of primary interest to this project scenario is the Global Climate Observing System (GCOS) and Global Ocean Observing System (GOOS), and the Joint Working Group on Climate (WG Climate) of the Committee on Earth Observation Satellites (CEOS). In-situ data sources are provided through a number of program initiatives sponsored through NOAA and provide key indicators for climate change that cannot be directly inferred from raw satellite information.
GCOS defines 54 Essential Climate Variables of which 18 ECVs apply to the oceans domain. Of these, only 6 ECVs may be inferred from satellite based earth observations while the remainder must be inferred through in-situ site observations and/or sampling programs.
The following table identifies the ocean-specific ECVs and associated providers.
Table 1
| Variable | Description | Source of Indicator |
|---|---|---|
| Ocean Colour | Provides indictation of phytoplankton based on Ocean Colour Radiance (OCR) | ESA CEOS |
| Carbon Dioxide Partial Pressure | Primary indicator of the exchange of CO2 at the ocean surface | NOAA |
| Ocean Acidity | pH of ocean water as measured at varying depths and locations | NOAA PMEL |
| Phytoplankton | Indicator of the health of the ecosystem associated with the food web and directly a result of increased CO2 and eutrophication | NOAA |
| Sea Ice | Sea ice coverage associated with the ocean surface and a concern reflected in warming surface temperatures and sea level rise | |
| Sea Level | Sea level global mean and variability leading to sea level rise | |
| Sea State | Wave height, direction, wavelength as indicators of energy at the ocean surface | |
| Sea-surface Salinity | The proportion of ocean water comprised of salt and indicator of mortality rates in shellfish | |
| Sea-surface Temperature | Directly affects major weather patterns and ecosystems | ESA CEOS; NOAA Monitoring Stations; NOAA Saildrone program |
| Surface Current | Transports heat, salt and passive tracers and has a large impact on seaborne commerce and fishing |
As well, social and economic key indicators related to the area of interest are ingested to identify relationships between the immediate effects of climate change on the associated human activity.
Table 2
| Variable | Description | Source of Indicator |
|---|---|---|
| AQ Landings | Annual yields associated with Aquaculture sites within a region of interest | MaineAQ |
| GDP | Gross Domestic Product ($USD) associated with dependent human activities within the region of interest | US Census |
| Employment | Number of individuals dependent on the targeted ecosystem | US Census |
| Population | Number of people inhabiting the area of interest associated with the ecosystem | US Census |
6.2.2. Approach
Each ECV applicable to the use case is resolved as a service endpoint representing the area of interest, associated samplings and observations, and where possible inferred from earth observation datasets transformed as ‘analysis ready’. Earth observation datasets are sourced through the ESA GCOS service endpoint; Ocean related samplings and in-situ observations are sourced through NOAA; socio-economic data is sourced from various open data portals available through government agencies.
The project effort centers around 3 key challenges * the ability to collect data relevant to Climate Resilience; * the ability to apply the data in a coherent and standardized manner in which to draw out context; * and the ability to impart insight to community members and stakeholders so as to identify, anticipate and mitigate the effects of climate change across both local and international boundaries.
Each of these activities aligns with the best practices and standards of the OGC and are used as input to the MarineDWG MSDI reference model.
Figure 50 — Architecture
6.3. ECMWF — Copernicus (will be integrated with INTRODUCTION section)
Component: Copernicus services.
Outputs: Copernicus Services, including Climate Data Store (CDS) https://cds.climate.copernicus.eu/ and Atmosphere Data Store (ADS) https://ads.atmosphere.copernicus.eu/.
What other component(s) can interact with the component: CDS and ADS provide access to data via different interfaces: UI and API. It also offers a toolbox with a set of expert libraries to perform advanced operations on the available data. CDS and ADS catalogue metadata is also accessible via standard CSW. https://cds.climate.copernicus.eu/geonetwork/srv/eng/csw?SERVICE=CSW=2.0.2=GetCapabilities
What OGC standards or formats does the component use and produce:
CDS and ADS catalogues exposed via CSW.
Access to ESGF datasets via WPS.
WMS is offered in some published applications.
CADS 2.0 (under construction) will implement OGC APIs.
6.3.1. DRI: Heat Impact and Drought Impact Components — Safe Software
6.3.1.1. Heat Impact DRI Component
This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated heat impacts over time, based on selected climate scenarios. Central to this is the identification of key heat impact indicators required by decision makers and the business rules needed to drive them. Process steps include data aggregation and statistical analysis of maximum temperature spikes, taking into account the cumulative impacts of multiple high temperature days. Heat exhaustion effects are likely dependent on duration of heat spells, in addition to high maximum temperatures on certain days.
Figure 51 — ARD Query: Monthly Max Temp Contours
Figure 52 — ARD Query: Max Mean Monthly Temp > 25C
Figure 53 — Town of Lytton - location where entire town was devastated by fire during the heat wave of July 2021 - same location highlighted in ARD query from heat risk query in previous figure
6.3.1.2. Drought Impact DRI Component
This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated drought risk impacts over time based on selected climate scenarios. It also feeds drought related environmental factors to other pilot DRI components for more refined drought risk analysis. For the purposes of this pilot, it was recognised that more complex indicators such as drought are likely driven by multiple environmental and physical factors. As such, our initial goal was to select and provide primary climate variable data that would be useful for deriving drought risks in combination with other inputs. Given that the primary input to drought models is precipitation, or lack thereof, we developed a data flow that extracted total precipitation per month and made this available both as a time series CSV and GeoJSON datasets, as well as OGC API features time series points. This climate scenario primary drought data was provided for the province of Manitoba and for Los Angelas. These two regions were chosen since we had pilot participants interested in each of these regions and in the case of Manitoba there is also a tie in to future work as this is an area of interest for the subsequent Disaster Pilot 2023.
For the LA use case, we worked with Laubwerk to provide them with climate change impact data that could help drive a drought impact that could affect their future landscape visualization model. The idea is that based on changes to climatic variables, certain areas may be more or less suited to different vegetation types, causing the distribution of vegetation to change over time. For more on their component, please refer to section 7: From Data To Visualization.
In the case of this visualization component, simply providing precipitation totals per month were not sufficient to drive the needs of their vegetation model. In this case we did not have an intermediate drought model to feed climate variables to. In the absence of a more comprehensive drought model, we decided to develop a proxy drought risk indicator by normalizing the difference between future precipitation and past.
Calculations were made using the difference between time series grids of projected precipitation and historical grids of mean precipitation per month. These precipitation deltas were then divided by the historical max — mean per month to derive a precipitation index. The goal was to provide a value between -1 and +1 where 1 = 100% of past mean precipitation for that month. Naturally this approach can generate values that exceed the range of -1 to 1 if the projected precipitation values exceed the historic max or min. The goal was not so much to predict future absolute precipitation values but rather generate an estimated for precipitation trends given the influence of climate change. For example, this approach can help answer the question — in 30 years for a given location, compare to historical norms, by what percentage do we expect precipitation to increase or decrease. Laubwerk can then take these results and decide what degree of drought stress will cause a specific vegetation species to die out for a particular location.
Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.
Figure 54 — FME Query Workflow: Geopackage precipitation delta time series to GeoJSON points
Figure 55 — FME Query Parameters: Geopackage precipitation delta time series to GeoJSON points
Figure 56 — FME Data Inspector: precipitation delta result showing potential drought risk for areas and times with significantly less precipitation than past
This approach is only a start and just scratches the surface in terms of what is possible for future drought projection based on climate model scenario ECVs. The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow. However one of the main goals of this pilot was not so much to produced highly refined forecast models for drought but rather to demonstrate the data value chain whereby raw climate model data cube outputs can feed a data pipeline that filters, refines, simplifies the data and ultimately can be used to drive indicators that help planners model visualize and understand the effects of climate change on the landscapes and environments within their communities.
To support future drought risk estimates for Manitoba, we also provided a precipitation forecast time series to Pixalytics as an input to their drought analytics and DRI component. Their component provides a much more sophisticated indicator of drought probability since in addition to precipitation it also takes into account soil moisture and vegetation. The goal was to extract precipitation totals per time step from the downscaled RCM — regional climate model ECV outputs for Manitoba based on CMIP5 (Coupled Model Intercomparison Project Phase 5) model results obtained from Environment Canada. For this use case the grids have a spatial resolution of roughly 10km and a temporal resolution a monthly time step. Pixalytics then ran their drought model based on these precipitation estimates in order to asses potential future drought risk in southern Manitoba. The data was provided to Pixalytics initially as a GeoJSON feed of 2d points derived from the data cube cells with precipitation totals per cell. We later also provided this same data feed as a OGC API Feature service.
For future phases of the climate or disaster pilots, it may be useful to explore additional approaches for both precipitation data analysis and combination with other related datasets and external models. It may be useful to segment cumulative rainfall below a certain threshold Pt within a certain time window (days, weeks or months), since cumulative rainfall over time will be crucial for computing water budgets by watershed or catch basin. To do this we would like to test the use of a higher resolution time step such as daily, to see if the increased resolution reveals patterns of interest that the coarser monthly time step does not. There are also other statistical RCM results that might be useful to make available (mean, min, max). Besides precipitation, climate models also generate soil moisture predictions which could used by this component to assess drought risk. This component would also benefit from integration with topography, DEMs and hydrology related data such as river networks, water bodies, aquifers and watershed boundaries. Therefore rather than just computing precipitation deltas at the cell level, it would likely be useful to sum precipitation by catch basin and compute future trends that may indicate potential drought or flood.
The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow.
It should be stressed that the field of drought modelling is not new and there are many drought modelling tools available that are far more sophisticated than anything described above. As such, subsequent Climate and Disaster pilots should explore how future climate projections can be funneled into these more mature climate models in an automated fashion to produce more refined estimates of projected drought risk. That said, we need to start somewhere, and it is hoped that this basic demonstration of the raw data to ARD to DRI value chain for drought can provide some insights into what type of indicators we may want to generate to help better understand future drought risks, and where we may want to improve on this process.
7. Future Work
Being the first OGC Climate Pilot, there has been significant underpinning work on the component elements that has supported an improved understanding of what is currently possible and what needs to be developed. Future pilots will focus on supporting the filling-in of identified gaps and definition of best practices guidelines to support and enable broader international partnerships.
During the pilot, participants agreed to the following items were specific actions where future work would be needed:
Further integration of the contributor components so that full workflows, from raw data to visualization and communication, can be tested.
Exploring additional scenario tests including comparisons with historical norms, e.g. calculating the difference between historical maximum temperatures and projected maximum temperatures.
More analysis with higher resolution time steps — weekly and daily. At the outset monthly time steps have been used to make it easier to prototype workflows. Daily time step computations will take significantly more processing time.
For ESRI’s contribution, the first version of CRMA was well received, it is widely used by the intended users and there is high interest by many others. Before the first version was released we had requests for other countries, and customizations of the project.
Due to many customization requests version 2 is being developed from inception with the intent for all code, from data processing python to web application Javascript to be available in Github repositories with documentation of typical customization workflows.
Use other climate projection data
Compute other indices
Summarize to other geographies
Customize the web application
The project is not only a solution, but a pattern for others to adapt to their data, geography, goals.
Version 2 data development is underway and will include more indices, both imperial and metric units, and min/max/mean for statistics instead of only areal mean. We will update all modeling to to CMIP6, and expand from US to global. Anticipated release in Q4 2023.
Annex A
(informative)
Revision History
| Date | Release | Author | Primary clauses modified | Description |
|---|---|---|---|---|
| 2023-03-28 | 0.1 | G. Schumann; A.J. Kettner | all | First draft of ER |
| 2023-03-29 | 0.2 | Nils Hempelmann | adapt to new ER scheema | revision draft of ER |
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